{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b3716f98",
   "metadata": {},
   "source": [
    "<h1 style=\"text-align: center;\">MCQ Creator App</h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4af634de",
   "metadata": {},
   "source": [
    "## Table of Contents\n",
    "* #### Install & Import Dependencies\n",
    "* #### Load Documents\n",
    "* #### Transformer Documents\n",
    "* #### Generate Text Embeddings\n",
    "* #### Vector store - PINECONE\n",
    "* #### Retrieve Answers\n",
    "* #### Structure the Output"
   ]
  },
  {
   "attachments": {
    "mcq%20langchain.PNG": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "115eb804",
   "metadata": {},
   "source": [
    "![mcq%20langchain.PNG](attachment:mcq%20langchain.PNG)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4f3323b",
   "metadata": {},
   "source": [
    "## Install Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "c3e48a77",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-28T09:02:10.646777900Z",
     "start_time": "2024-01-28T09:02:10.452737900Z"
    }
   },
   "outputs": [],
   "source": [
    "#!pip install unstructured\n",
    "#!pip install tiktoken\n",
    "#!pip install pinecone-client\n",
    "#!pip install pypdf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0348a82f",
   "metadata": {},
   "source": [
    "## Import Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "8b382dd0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-28T09:02:10.785819700Z",
     "start_time": "2024-01-28T09:02:10.461775900Z"
    }
   },
   "outputs": [],
   "source": [
    "import openai\n",
    "import pinecone\n",
    "from langchain.document_loaders import PyPDFDirectoryLoader\n",
    "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
    "from langchain.vectorstores import Pinecone\n",
    "from langchain.llms import OpenAI\n",
    "from langchain.embeddings.sentence_transformer import SentenceTransformerEmbeddings"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99bb6019",
   "metadata": {},
   "source": [
    "<font color='green'>\n",
    "The code sets environment variables for accessing OpenAI API and Hugging Face Hub API using respective API keys<font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "7e40e7b3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-28T09:02:10.790815900Z",
     "start_time": "2024-01-28T09:02:10.477736900Z"
    }
   },
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "# os.environ['OPENAI_API_KEY'] = \"\"\n",
    "# os.environ['OPENAI_API_BASE'] = \"\"\n",
    "# os.environ['HUGGINGFACEHUB_API_TOKEN'] = ''"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c1fc352",
   "metadata": {},
   "source": [
    "## Load Documents"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b5e5fd9",
   "metadata": {},
   "source": [
    "<font color='green'>\n",
    "Loads PDF files available in a directory with pypdf<font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "2ffc8399",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-28T09:02:10.801816800Z",
     "start_time": "2024-01-28T09:02:10.491739500Z"
    }
   },
   "outputs": [],
   "source": [
    "#Function to read documents\n",
    "def load_docs(directory):\n",
    "  loader = PyPDFDirectoryLoader(directory)\n",
    "  documents = loader.load()\n",
    "  return documents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "89d28269",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-28T09:02:10.914773700Z",
     "start_time": "2024-01-28T09:02:10.507736700Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "3"
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Passing the directory to the 'load_docs' function\n",
    "directory = 'Docs/'\n",
    "documents = load_docs(directory)\n",
    "len(documents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "28b7fc73",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-28T09:02:10.915738200Z",
     "start_time": "2024-01-28T09:02:10.666737900Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "[Document(page_content=\"India, officially known as the Republic of India, is a diverse and vibrant country located in South\\nAsia. With a rich history spanning thousands of years, India is known for its cultural heritage, \\nreligious diversity, and vast landscapes. From the majestic Himalayas in the north to the serene\\nbackwaters of Kerala in the south, India encompasses a wide range of geographical features, \\nincluding deserts, plains, mountains, and coastlines, making it a land of incredible natural \\nbeauty.\\nIndia is the seventh-largest country by land area and the second-most populous country in the \\nworld, with a population exceeding 1.3 billion people. It is a federal parliamentary democratic \\nrepublic, with a president as the head of state and a prime minister as the head of government. \\nThe country follows a multi-tiered administrative structure, with 28 states and 9 union territories,\\neach having its own elected government.\\nIndia has a rich cultural heritage that has evolved over thousands of years. It is home to various\\nreligions, including Hinduism, Islam, Christianity, Sikhism, Buddhism, and Jainism, among \\nothers. These religions coexist harmoniously, contributing to India's multicultural fabric. \\nFestivals like Diwali, Eid, Christmas, and Holi are celebrated with great enthusiasm and bring \\npeople from different communities together.\\nThe history of India is characterized by ancient civilizations, invasions, and the establishment of\\npowerful empires. The Indus Valley Civilization, one of the world's oldest urban civilizations, \\nflourished in the northwestern part of the Indian subcontinent around 2500 BCE. Over the \\ncenturies, India witnessed the rise and fall of several dynasties, including the Maurya, Gupta, \\nand Mughal empires. The Mughal period, in particular, left a lasting impact on Indian culture, \\nart, and architecture.\\nIndia's struggle for independence from British colonial rule is a significant chapter in its history. \\nLed by Mahatma Gandhi and other freedom fighters, the non-violent resistance movement \\ngained momentum and eventually led to India's independence on August 15, 1947. This day is \\ncelebrated annually as Independence Day.\\nIndia's economy is one of the fastest-growing in the world. It has transitioned from an agrarian \\neconomy to a service-oriented and industrialized economy. The country is known for its \\nsoftware and information technology services, pharmaceuticals, textiles, agriculture, and \\nmanufacturing sectors. Major cities like Mumbai, Delhi, Bangalore, and Chennai are hubs of \\nbusiness and commerce, attracting investments and fostering innovation.\\nDelhi is the capital of India\", metadata={'source': 'Docs\\\\Doc 1.pdf', 'page': 0}),\n Document(page_content=\"However, India also faces various socio-economic challenges. Poverty, income inequality, and \\nunemployment are persistent issues that the country strives to address. Efforts are being made\\nto improve education, healthcare, infrastructure, and social welfare programs to uplift \\nmarginalized sections of society.\\nEducation plays a vital role in India, with a strong emphasis on academic excellence. The \\ncountry has a vast network of schools, colleges, and universities, producing a large number of \\ngraduates every year. Indian professionals have made significant contributions in various fields \\nglobally, particularly in science, technology, engineering, and mathematics (STEM).\\nThe Indian film industry, popularly known as Bollywood, is a global phenomenon, producing the\\nlargest number of films annually. Indian cinema reflects the diversity and cultural richness of \\nthe country and has a massive following both within India and among the Indian diaspora \\nworldwide.\\nIndian cuisine is renowned for its flavors, spices, and regional specialties. Each state has its \\nown culinary traditions, offering a wide range of vegetarian and non-vegetarian dishes. Indian \\nfood has gained international popularity, with dishes like curry, biryani, dosa, and tandoori \\nbeing enjoyed by people worldwide.\\nThe Indian rupee is the official currency in the Republic of India. The rupee is subdivided into \\n100 paise. The issuance of the currency is controlled by the Reserve Bank of India.\\n₹ The Indian rupee sign ( ) is the currency symbol for the Indian rupee the official currency of \\nIndia\\nTourism is a significant contributor to India's economy. The country attracts millions of visitors \\neach year who come to explore its historical sites, architectural wonders, wildlife sanctuaries, \\nand scenic landscapes. Iconic landmarks such as the Taj Mahal, Jaipur's palaces, Kerala's \\nbackwaters, and the beaches of Goa are popular tourist destinations.\\nIndia's cultural heritage is preserved in its ancient monuments and UNESCO World Heritage \\nSites. From the intricate carvings of Khajuraho temples to the majestic forts of Rajasthan, these\\narchitectural marvels reflect India's rich history and artistic traditions.\\nIndia's diversity extends to its languages as well. While Hindi and English are the official \\nlanguages at the national level, there are 22 officially recognized regional languages, including \\nBengali, Tamil, Telugu, Marathi, Urdu, Punjabi, and Gujarati, among others. This linguistic \\ndiversity is a testament to India's multicultural ethos.\\nIn recent years, India has made significant strides in space exploration. The Indian Space \\nResearch Organization (ISRO) has successfully launched satellites and missions, including the\\nMars Orbiter Mission (MOM), also known as Mangalyaan. These achievements have placed \\nIndia among the elite group of nations with advanced space programs.\", metadata={'source': 'Docs\\\\Doc 2.pdf', 'page': 0}),\n Document(page_content=\"India's diplomatic influence is also growing on the global stage. The country actively \\nparticipates in international forums and has strong bilateral relations with nations around the \\nworld. India is a founding member of the Non-Aligned Movement and plays an active role in \\nvarious international organizations, such as the United Nations and World Trade Organization.\\nIn conclusion, India is a vast and diverse country with a rich cultural heritage, stunning \\nlandscapes, and a rapidly growing economy. It is a nation where ancient traditions coexist with \\nmodern aspirations. Despite its challenges, India continues to evolve and leave an indelible \\nmark on the world, making it a fascinating and dynamic country to explore.\", metadata={'source': 'Docs\\\\Doc 2.pdf', 'page': 1})]"
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "documents"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fee4770d",
   "metadata": {},
   "source": [
    "## Transform Documents"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79688cbe",
   "metadata": {},
   "source": [
    "<font color='green'>\n",
    "Split document Into Smaller Chunks<font>"
   ]
  },
  {
   "attachments": {
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"
    }
   },
   "cell_type": "markdown",
   "id": "5e487274",
   "metadata": {},
   "source": [
    "![6302455.png](attachment:6302455.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "63503325",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-28T09:02:10.915738200Z",
     "start_time": "2024-01-28T09:02:10.683736100Z"
    }
   },
   "outputs": [],
   "source": [
    "#This function will split the documents into chunks\n",
    "def split_docs(documents, chunk_size=1000, chunk_overlap=20):\n",
    "  text_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap)\n",
    "  docs = text_splitter.split_documents(documents)\n",
    "  return docs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "d8a00b05",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-28T09:02:10.915738200Z",
     "start_time": "2024-01-28T09:02:10.698739300Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7\n"
     ]
    }
   ],
   "source": [
    "docs = split_docs(documents)\n",
    "print(len(docs))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25244e9c",
   "metadata": {},
   "source": [
    "## Generate Text Embeddings"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f59d133",
   "metadata": {},
   "source": [
    "<font color='green'>\n",
    "OpenAI LLM for creating Embeddings for documents/Text<font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "3268ec68",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-28T09:02:13.495279200Z",
     "start_time": "2024-01-28T09:02:10.713736500Z"
    }
   },
   "outputs": [],
   "source": [
    "embeddings = OpenAIEmbeddings()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42fe08aa",
   "metadata": {},
   "source": [
    "<font color='green'>\n",
    "Hugging Face LLM for creating Embeddings for documents/Text<font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "60744d3f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-28T09:02:13.507471600Z",
     "start_time": "2024-01-28T09:02:13.496395200Z"
    }
   },
   "outputs": [],
   "source": [
    "# embeddings = SentenceTransformerEmbeddings(model_name=\"all-MiniLM-L6-v2\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29ce78a6",
   "metadata": {},
   "source": [
    "<font color='green'>\n",
    "Let's test our Embeddings model for a sample text<font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "38387cb0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-28T09:02:17.263230600Z",
     "start_time": "2024-01-28T09:02:13.508742100Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "1536"
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query_result = embeddings.embed_query(\"Hello Buddy\")\n",
    "len(query_result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "26ca5583",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-28T09:02:17.334594600Z",
     "start_time": "2024-01-28T09:02:17.266231300Z"
    }
   },
   "outputs": [
    {
     "data": {
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     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query_result"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d422eaa",
   "metadata": {},
   "source": [
    "## Vector store - PINECONE"
   ]
  },
  {
   "attachments": {
    "pinecone.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "8f0a248e",
   "metadata": {},
   "source": [
    "![pinecone.png](attachment:pinecone.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d6a7621",
   "metadata": {},
   "source": [
    "<font color='green'>\n",
    "Pinecone allows for data to be uploaded into a vector database and true semantic search can be performed.<br><br> Not only is conversational data highly unstructured, but it can also be complex. Vector search and vector databases allows for similarity searches.<font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c84fd4c",
   "metadata": {},
   "source": [
    "<font color='green'>\n",
    "We will initialize Pinecone and create a Pinecone index by passing our documents, embeddings model and mentioning the specific INDEX which has to be used\n",
    "    \n",
    "Vector databases are designed to handle the unique structure of vector embeddings, which are dense vectors of numbers that represent text. They are used in machine learning to capture the meaning of words and map their semantic meaning. <br><br>These databases index vectors for easy search and retrieval by comparing values and finding those that are most similar to one another, making them ideal for natural language processing and AI-driven applications.\n",
    "    <font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "821ddd43",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-28T09:02:17.338538700Z",
     "start_time": "2024-01-28T09:02:17.293541Z"
    }
   },
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "init is no longer a top-level attribute of the pinecone package.\n\nPlease create an instance of the Pinecone class instead.\n\nExample:\n\n    import os\n    from pinecone import Pinecone, ServerlessSpec\n\n    pc = Pinecone(\n        api_key=os.environ.get(\"PINECONE_API_KEY\")\n    )\n\n    # Now do stuff\n    if 'my_index' not in pc.list_indexes().names():\n        pc.create_index(\n            name='my_index', \n            dimension=1536, \n            metric='euclidean',\n            spec=ServerlessSpec(\n                cloud='aws',\n                region='us-west-2'\n            )\n        )\n\n",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mAttributeError\u001B[0m                            Traceback (most recent call last)",
      "Cell \u001B[1;32mIn[49], line 1\u001B[0m\n\u001B[1;32m----> 1\u001B[0m \u001B[43mpinecone\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43minit\u001B[49m\u001B[43m(\u001B[49m\n\u001B[0;32m      2\u001B[0m \u001B[43m    \u001B[49m\u001B[43mapi_key\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43md3104bf6-cee8-4412-af4a-f87e055ad154\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[0;32m      3\u001B[0m \u001B[43m    \u001B[49m\u001B[43menvironment\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mus-west1-gcp-free\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\n\u001B[0;32m      4\u001B[0m \u001B[43m)\u001B[49m\n\u001B[0;32m      6\u001B[0m index_name \u001B[38;5;241m=\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mmcq-creator\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m      8\u001B[0m index \u001B[38;5;241m=\u001B[39m Pinecone\u001B[38;5;241m.\u001B[39mfrom_documents(docs, embeddings, index_name\u001B[38;5;241m=\u001B[39mindex_name)\n",
      "File \u001B[1;32mF:\\anaconda\\envs\\py39f\\lib\\site-packages\\pinecone\\deprecation_warnings.py:38\u001B[0m, in \u001B[0;36minit\u001B[1;34m(*args, **kwargs)\u001B[0m\n\u001B[0;32m     11\u001B[0m     example \u001B[38;5;241m=\u001B[39m \u001B[38;5;124m\"\"\"\u001B[39m\n\u001B[0;32m     12\u001B[0m \u001B[38;5;124m    import os\u001B[39m\n\u001B[0;32m     13\u001B[0m \u001B[38;5;124m    from pinecone import Pinecone, ServerlessSpec\u001B[39m\n\u001B[1;32m   (...)\u001B[0m\n\u001B[0;32m     29\u001B[0m \u001B[38;5;124m        )\u001B[39m\n\u001B[0;32m     30\u001B[0m \u001B[38;5;124m\"\"\"\u001B[39m\n\u001B[0;32m     31\u001B[0m     msg \u001B[38;5;241m=\u001B[39m \u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\"\"\u001B[39m\u001B[38;5;124minit is no longer a top-level attribute of the pinecone package.\u001B[39m\n\u001B[0;32m     32\u001B[0m \n\u001B[0;32m     33\u001B[0m \u001B[38;5;124mPlease create an instance of the Pinecone class instead.\u001B[39m\n\u001B[1;32m   (...)\u001B[0m\n\u001B[0;32m     36\u001B[0m \u001B[38;5;132;01m{\u001B[39;00mexample\u001B[38;5;132;01m}\u001B[39;00m\n\u001B[0;32m     37\u001B[0m \u001B[38;5;124m\"\"\"\u001B[39m\n\u001B[1;32m---> 38\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mAttributeError\u001B[39;00m(msg)\n",
      "\u001B[1;31mAttributeError\u001B[0m: init is no longer a top-level attribute of the pinecone package.\n\nPlease create an instance of the Pinecone class instead.\n\nExample:\n\n    import os\n    from pinecone import Pinecone, ServerlessSpec\n\n    pc = Pinecone(\n        api_key=os.environ.get(\"PINECONE_API_KEY\")\n    )\n\n    # Now do stuff\n    if 'my_index' not in pc.list_indexes().names():\n        pc.create_index(\n            name='my_index', \n            dimension=1536, \n            metric='euclidean',\n            spec=ServerlessSpec(\n                cloud='aws',\n                region='us-west-2'\n            )\n        )\n\n"
     ]
    }
   ],
   "source": [
    "pinecone.init(\n",
    "    api_key=\"d3104bf6-cee8-4412-af4a-f87e055ad154\",\n",
    "    environment=\"us-west1-gcp-free\"\n",
    ")\n",
    "\n",
    "index_name = \"mcq-creator\"\n",
    "\n",
    "index = Pinecone.from_documents(docs, embeddings, index_name=index_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76d84861",
   "metadata": {},
   "source": [
    "## Retrieve Answers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f46834ca",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-28T09:02:17.352541100Z",
     "start_time": "2024-01-28T09:02:17.339538600Z"
    }
   },
   "outputs": [],
   "source": [
    "#This function will help us in fetching the top relevent documents from our vector store - Pinecone\n",
    "def get_similiar_docs(query, k=2):\n",
    "    similar_docs = index.similarity_search(query, k=k)\n",
    "    return similar_docs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b458a0b1",
   "metadata": {},
   "source": [
    "<font color='green'>\n",
    "'load_qa_chain' Loads a chain that you can use to do QA over a set of documents.<br>\n",
    "    And we will be using Huggingface for the reasoning purpose\n",
    "<font"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4923071d",
   "metadata": {
    "ExecuteTime": {
     "start_time": "2024-01-28T09:02:17.343544400Z"
    }
   },
   "outputs": [],
   "source": [
    "from langchain.chains.question_answering import load_qa_chain\n",
    "from langchain import HuggingFaceHub"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac5bb574",
   "metadata": {},
   "source": [
    "<font color='green'>\n",
    "BigScience Large Open-science Open-access Multilingual Language Model (BLOOM) is a transformer-based large language model.<br> <br>It was created by over 1000 AI researchers to provide a free large language model for everyone who wants to try. Trained on around 366 billion tokens over March through July 2022, it is considered an alternative to OpenAI's GPT-3 with its 176 billion parameters.\n",
    "<font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d4385ada",
   "metadata": {
    "ExecuteTime": {
     "start_time": "2024-01-28T09:02:17.346566300Z"
    }
   },
   "outputs": [],
   "source": [
    "llm=HuggingFaceHub(repo_id=\"bigscience/bloom\", model_kwargs={\"temperature\":1e-10})\n",
    "llm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "25a81d6e",
   "metadata": {
    "ExecuteTime": {
     "start_time": "2024-01-28T09:02:17.349546400Z"
    }
   },
   "outputs": [],
   "source": [
    "#llm = OpenAI()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe21fc1a",
   "metadata": {},
   "source": [
    "<font color='green'>\n",
    "Different Types Of Chain_Type:<br><br>\n",
    "\"map_reduce\": It divides the texts into batches, processes each batch separately with the question, and combines the answers to provide the final answer.<br>\n",
    "\"refine\": It divides the texts into batches and refines the answer by sequentially processing each batch with the previous answer.<br>\n",
    "\"map-rerank\": It divides the texts into batches, evaluates the quality of each answer from LLM, and selects the highest-scoring answers from the batches to generate the final answer. These alternatives help handle token limitations and improve the effectiveness of the question-answering process.\n",
    "<font"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "71d37723",
   "metadata": {
    "ExecuteTime": {
     "start_time": "2024-01-28T09:02:17.351536200Z"
    }
   },
   "outputs": [],
   "source": [
    "chain = load_qa_chain(llm, chain_type=\"stuff\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6eee957a",
   "metadata": {
    "ExecuteTime": {
     "start_time": "2024-01-28T09:02:17.353537900Z"
    }
   },
   "outputs": [],
   "source": [
    "#This function will help us get the answer to the question that we raise\n",
    "def get_answer(query):\n",
    "  relevant_docs = get_similiar_docs(query)\n",
    "  print(relevant_docs)\n",
    "  response = chain.run(input_documents=relevant_docs, question=query)\n",
    "  return response"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3c22b2c",
   "metadata": {},
   "source": [
    "<font color='green'>\n",
    "Let's pass our question to the above created function\n",
    "<font"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d9274da2",
   "metadata": {
    "scrolled": true,
    "ExecuteTime": {
     "start_time": "2024-01-28T09:02:17.356538Z"
    }
   },
   "outputs": [],
   "source": [
    "our_query = \"How is India's economy?\"\n",
    "answer = get_answer(our_query)\n",
    "print(answer)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6adf2ccd",
   "metadata": {},
   "source": [
    "## Structure the Output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9f5bce3a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-28T09:02:17.432663200Z",
     "start_time": "2024-01-28T09:02:17.359537500Z"
    }
   },
   "outputs": [],
   "source": [
    "import re\n",
    "import json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2559478b",
   "metadata": {
    "ExecuteTime": {
     "start_time": "2024-01-28T09:02:17.363539800Z"
    }
   },
   "outputs": [],
   "source": [
    "from langchain.chat_models import ChatOpenAI\n",
    "from langchain.schema import HumanMessage\n",
    "from langchain.prompts import PromptTemplate, ChatPromptTemplate, HumanMessagePromptTemplate\n",
    "from langchain.output_parsers import StructuredOutputParser, ResponseSchema"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d309c532",
   "metadata": {
    "ExecuteTime": {
     "start_time": "2024-01-28T09:02:17.366536Z"
    }
   },
   "outputs": [],
   "source": [
    "response_schemas = [\n",
    "    ResponseSchema(name=\"question\", description=\"Question generated from provided input text data.\"),\n",
    "    ResponseSchema(name=\"choices\", description=\"Available options for a multiple-choice question in comma separated.\"),\n",
    "    ResponseSchema(name=\"answer\", description=\"Correct answer for the asked question.\")\n",
    "]\n",
    "\n",
    "output_parser = StructuredOutputParser.from_response_schemas(response_schemas)\n",
    "output_parser"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "32b73a3d",
   "metadata": {
    "ExecuteTime": {
     "start_time": "2024-01-28T09:02:17.369537200Z"
    }
   },
   "outputs": [],
   "source": [
    "# This helps us fetch the instructions the langchain creates to fetch the response in desired format\n",
    "format_instructions = output_parser.get_format_instructions()\n",
    " \n",
    "print(format_instructions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2740a1fd",
   "metadata": {
    "ExecuteTime": {
     "start_time": "2024-01-28T09:02:17.371536Z"
    }
   },
   "outputs": [],
   "source": [
    "# create ChatGPT object\n",
    "chat_model = ChatOpenAI()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b41f0637",
   "metadata": {
    "ExecuteTime": {
     "start_time": "2024-01-28T09:02:17.373536Z"
    }
   },
   "outputs": [],
   "source": [
    "chat_model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95c77a73",
   "metadata": {},
   "source": [
    "<font color='green'>\n",
    "The below snippet will give out a string that contains instructions for how the response should be formatted, and we then insert that into our prompt.\n",
    "<font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "56b47e84",
   "metadata": {
    "ExecuteTime": {
     "start_time": "2024-01-28T09:02:17.374536500Z"
    }
   },
   "outputs": [],
   "source": [
    "prompt = ChatPromptTemplate(\n",
    "    messages=[\n",
    "        HumanMessagePromptTemplate.from_template(\"\"\"When a text input is given by the user, please generate multiple choice questions \n",
    "        from it along with the correct answer. \n",
    "        \\n{format_instructions}\\n{user_prompt}\"\"\")  \n",
    "    ],\n",
    "    input_variables=[\"user_prompt\"],\n",
    "    partial_variables={\"format_instructions\": format_instructions}\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ca797627",
   "metadata": {
    "ExecuteTime": {
     "start_time": "2024-01-28T09:02:17.376537800Z"
    }
   },
   "outputs": [],
   "source": [
    "final_query = prompt.format_prompt(user_prompt = answer)\n",
    "print(final_query)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2ed162b9",
   "metadata": {
    "ExecuteTime": {
     "start_time": "2024-01-28T09:02:17.379537400Z"
    }
   },
   "outputs": [],
   "source": [
    "final_query.to_messages()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "99f7ca80",
   "metadata": {
    "ExecuteTime": {
     "start_time": "2024-01-28T09:02:17.380536400Z"
    }
   },
   "outputs": [],
   "source": [
    "final_query_output = chat_model(final_query.to_messages())\n",
    "print(final_query_output.content)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b7f64a5",
   "metadata": {},
   "source": [
    "<font color='green'>\n",
    "While working with scenarios like above where we have to process multi-line strings(separated by newline characters – ‘\\n’). In such situations, we use re.DOTALL.\n",
    "<font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "11def92b",
   "metadata": {
    "ExecuteTime": {
     "start_time": "2024-01-28T09:02:17.382551900Z"
    }
   },
   "outputs": [],
   "source": [
    "# Let's extract JSON data from Markdown text that we have\n",
    "markdown_text = final_query_output.content\n",
    "json_string = re.search(r'{(.*?)}', markdown_text, re.DOTALL).group(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d4db4592",
   "metadata": {
    "ExecuteTime": {
     "start_time": "2024-01-28T09:02:17.383535600Z"
    }
   },
   "outputs": [],
   "source": [
    "print(json_string)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7932fff5",
   "metadata": {
    "ExecuteTime": {
     "start_time": "2024-01-28T09:02:17.385536700Z"
    }
   },
   "outputs": [],
   "source": []
  }
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